The case for data visualization management systems

Wu, Eugene; Battle, Leilani; Madden, Samuel

doi:10.14778/2732951.2732964

Cited by 64 publications

(33 citation statements)

References 26 publications

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“…Data visualization management systems that automate and recommend visualizations to users have recently become a topic of active interest in the database and humancomputer interaction community [69]. Recent systems including SeeDB [50], Voyager [67], and ZenVisage [59] focus on recommending visualizations for large-scale data sets, particularly for exploratory analysis of relational data [47].…”

Section: Related Workmentioning

confidence: 99%

Asap

Rong¹,

Bailis²

2017

Proc. VLDB Endow.

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Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and ondemand refresh. We demonstrate that ASAP can improve users' accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.

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Section: Related Workmentioning

confidence: 99%

Asap

Rong¹,

Bailis²

2017

Proc. VLDB Endow.

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“…These approaches include query result reduction allowing for interactive visualization [11], rapid order preserving sampling [12] and search for automatically identifying interesting data visualizations [49]. We describe these optimizations as well as vision work that proposes a novel declarative visualization language [66] to bridge the gap between traditional database optimizations and visualization.…”

Section: User Interactionmentioning

confidence: 99%

“…Such novel requirements of modern exploration driven interfaces have led to rethinking of database systems across the whole stack, from storage to user interaction. Visualization tools for data exploration (e.g., [38,49,66]) are receiving growing interest while new exploration interfaces emerged (e.g., [18,32,45,57]) aiming to facilitate the user's interactions with the underlying database. In parallel, numerous novel optimizations have been proposed for offering interactive exploration times (e.g., [6,36,37]) while the database architecture has been re-examined to match the characteristics of the new exploration workloads (e.g., [8,27,28,39]).…”

Section: Introductionmentioning

confidence: 99%

Overview of Data Exploration Techniques

Idreos

Papaemmanouil

Chaudhuri

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

188

102

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Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions.

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“…Many systems were designed to visualize non-spatial big data (e.g., [3], [16], [34]- [36]) by downsizing the data, using sampling or aggregation, and then visualizing the downsized data on a single machine as a chart or histogram. For example, Ermac [36] suggests inject ing the visualization algorithms in the database engine so that sampling and aggregation are done early in the query plan. Similarly, M4 [16] rewrites SQL queries taking into account the limited size of the generated image to perform aggregation inside the database and return a small result size.…”

Section: Related Workmentioning

confidence: 99%